Translating configurations between different network devices is a common yet challenging task in modern network operations. This challenge arises in typical scenarios such as replacing obsolete hardware and adapting configurations to emerging paradigms like Software Defined Networking (SDN) and Network Function Virtualization (NFV). Engineers need to thoroughly understand both source and target configuration models, which requires considerable effort due to the complexity and evolving nature of these specifications. To promote automation in network configuration translation, we propose INTA, an intent-based translation framework that leverages Large Language Model (LLM) agents. The key idea of INTA is to use configuration intent as an intermediate representation for translation. It first employs LLMs to decompose configuration files and extract fine-grained intents for each configuration fragment. These intents are then used to retrieve relevant manuals of the target device. Guided by a syntax checker, INTA incrementally generates target configurations. The translated configurations are further verified and refined for semantic consistency. We implement INTA and evaluate it on real-world configuration datasets from the industry. Our approach outperforms state-of-the-art methods in translation accuracy and exhibits strong generalizability. INTA achieves an accuracy of 98.15% in terms of both syntactic and view correctness, and a command recall rate of 84.72% for the target configuration. The semantic consistency report of the translated configuration further demonstrates its practical value in real-world network operations.

The Automatic Certificate Management Environment (ACME) protocol automates the issuance and renewal of secure socket layer certificates, simplifying the management of large-scale certificate deployments. To reduce the load on Certificate Authority (CA) servers, ACME employs a caching mechanism that stores domain validation (DV) results for 30 days. However, this mechanism allows attackers to reuse previously authorized results, potentially bypassing the DV process. In this paper, we introduce the ACME Authz Cache Attack, whereby an attacker can obtain fraudulent certificates without domain control. We demonstrate that even the prominent CA, Let's Encrypt, is vulnerable to this attack. To mitigate this, we propose ACME++, an enhanced protocol that binds the client's IP address and a unique identifier to the ACME account, ensuring secure authorization for each new client and effectively preventing the ACME Authz Cache Attack. Our implementation of ACME++ shows that it introduces little overhead to the CA server.

Most existing deep learning models for remote sensing scene classification (RSSC) adopt offline learning paradigm, which are trained on closed datasets and fail to dynamically update with new class data. Currently, class-incremental learning (CIL) allows models to learn new classes while retaining discrimination of old ones. However, most CIL approaches aim to overcome catastrophic forgetting by employing techniques such as exemplarmemory and knowledge distillation, while ignoring the prediction bias caused by imbalanced datasets, where old classes retain fewer samples than new ones. Moreover, they do not adequately account for the multilevel semantic structure and multiscale feature information inherent in remote sensing images (RSIs). To address these issues, we propose an effective CIL framework for RSSC, named class bias correction network (CBCNet). Specifically, a cross-dimensional and interaction-aware attention mechanism (CIAM) is designed to incorporate channel, position, and direction-aware information in feature maps, enabling the model to highlight informative regions within RSIs. Next, a contextual information fusion module (CIFM) is proposed to explore the correlations among multilevel features and enhance representation quality through their fusion. In addition, the designed taskwise classifier head decoupling mechanism (TCDM) imposes a constraint to mitigate the prediction bias toward new classes, and enhances model’s discrimination among all seen classes. Finally, a multilevel integrated knowledge distillation module (MKDM) is developed to ensure comprehensive knowledge transfer, empowering the model to maintain critical representations in feature space and make well-informed decisions in output probability space. Experiments on five open datasets demonstrate the outperformance and robustness of our method.

Today’s network is notorious for its complexity and uncertainty. Network operators often rely on network models for efficient network planning, operation, and optimization. The network model is responsible for understanding the complex relationships between network performance metrics (e.g., delay and jitter) and network characteristics (e.g., traffic and configuration). However, we still lack a systematic approach to developing accurate and lightweight network models that are aware of the impact of network configurations (i.e., expressiveness) and provide fine-grained flow-level temporal predictions (i.e., granularity). In this paper, we propose xNet, a data-driven network modeling framework based on graph neural networks (GNN). It is worth noting that xNet is not a dedicated network model designed for a specific network scenario with constraint considerations. On the contrary, xNet provides a general approach to modeling the network characteristics of concern with relation graph representations and configurable GNN blocks. xNet learns the state transition functions between time steps and rolls them out to obtain the full fine-grained prediction trajectory. We implement and instantiate xNet with three use cases. The experimental results show that xNet can accurately predict different performance metrics (i.e. temporal and steady-state QoS) in different scenarios, with performance comparable to state-of-the-art domain-specific models. Compared with traditional packet-level simulators, xNet achieves a speed improvement of more than two orders of magnitude, demonstrating its promising application in real-time optimization of network configurations.